diff --git a/src/model_modules/inception_model.py b/src/model_modules/inception_model.py index 6467b3245ad097af6ef17e596f85264eef383d7a..15739556d7d28d9e7e6ecc454615d82fb81a2754 100644 --- a/src/model_modules/inception_model.py +++ b/src/model_modules/inception_model.py @@ -75,12 +75,9 @@ class InceptionModelBase: name=f'Block_{self.number_of_blocks}{self.block_part_name()}_1x1')(input_x) tower = self.act(tower, activation, **act_settings) - # tower = self.padding_layer(padding)(padding=padding_size, - # name=f'Block_{self.number_of_blocks}{self.block_part_name()}_Pad' - # )(tower) tower = Padding2D(padding)(padding=padding_size, - name=f'Block_{self.number_of_blocks}{self.block_part_name()}_Pad' - )(tower) + name=f'Block_{self.number_of_blocks}{self.block_part_name()}_Pad' + )(tower) tower = layers.Conv2D(tower_filter, tower_kernel, @@ -111,29 +108,6 @@ class InceptionModelBase: else: return act_name.__name__ - # @staticmethod - # def padding_layer(padding): - # allowed_paddings = { - # 'RefPad2D': ReflectionPadding2D, 'ReflectionPadding2D': ReflectionPadding2D, - # 'SymPad2D': SymmetricPadding2D, 'SymmetricPadding2D': SymmetricPadding2D, - # 'ZeroPad2D': keras.layers.ZeroPadding2D, 'ZeroPadding2D': keras.layers.ZeroPadding2D - # } - # if isinstance(padding, str): - # try: - # pad2d = allowed_paddings[padding] - # except KeyError as einfo: - # raise NotImplementedError( - # f"`{einfo}' is not implemented as padding. " - # "Use one of those: i) `RefPad2D', ii) `SymPad2D', iii) `ZeroPad2D'") - # else: - # if padding in allowed_paddings.values(): - # pad2d = padding - # else: - # raise TypeError(f"`{padding.__name__}' is not a valid padding layer type. " - # "Use one of those: " - # "i) ReflectionPadding2D, ii) SymmetricPadding2D, iii) ZeroPadding2D") - # return pad2d - def create_pool_tower(self, input_x, pool_kernel, tower_filter, activation='relu', max_pooling=True, **kwargs): """ This function creates a "MaxPooling tower block" @@ -159,7 +133,6 @@ class InceptionModelBase: block_type = "AvgPool" pooling = layers.AveragePooling2D - # tower = self.padding_layer(padding)(padding=padding_size, name=block_name+'Pad')(input_x) tower = Padding2D(padding)(padding=padding_size, name=block_name+'Pad')(input_x) tower = pooling(pool_kernel, strides=(1, 1), padding='valid', name=block_name+block_type)(tower) @@ -215,35 +188,6 @@ class InceptionModelBase: return block -# if __name__ == '__main__': -# from keras.models import Model -# from keras.layers import Conv2D, Flatten, Dense, Input -# import numpy as np -# -# -# kernel_1 = (3, 3) -# kernel_2 = (5, 5) -# x = np.array(range(2000)).reshape(-1, 10, 10, 1) -# y = x.mean(axis=(1, 2)) -# -# x_input = Input(shape=x.shape[1:]) -# pad1 = PadUtils.get_padding_for_same(kernel_size=kernel_1) -# x_out = InceptionModelBase.padding_layer('RefPad2D')(padding=pad1, name="RefPAD1")(x_input) -# # x_out = ReflectionPadding2D(padding=pad1, name="RefPAD")(x_input) -# x_out = Conv2D(5, kernel_size=kernel_1, activation='relu')(x_out) -# -# pad2 = PadUtils.get_padding_for_same(kernel_size=kernel_2) -# x_out = InceptionModelBase.padding_layer(SymmetricPadding2D)(padding=pad2, name="SymPAD1")(x_out) -# # x_out = SymmetricPadding2D(padding=pad2, name="SymPAD")(x_out) -# x_out = Conv2D(2, kernel_size=kernel_2, activation='relu')(x_out) -# x_out = Flatten()(x_out) -# x_out = Dense(1, activation='linear')(x_out) -# -# model = Model(inputs=x_input, outputs=x_out) -# model.compile('adam', loss='mse') -# model.summary() -# # model.fit(x, y, epochs=10) - if __name__ == '__main__': print(__name__) from keras.datasets import cifar10